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- Title
A Multigrid Nonlinear Least Squares Four‐Dimensional Variational Data Assimilation Scheme With the Advanced Research Weather Research and Forecasting Model.
- Authors
Zhang, Hongqin; Tian, Xiangjun
- Abstract
Abstract: The motions of the atmosphere have multiscale properties in space and/or time, and the background error covariance matrix ( Β) should thus contain error information at different correlation scales. To obtain an optimal analysis, the multigrid three‐dimensional variational data assimilation scheme is used widely when sequentially correcting errors from large to small scales. However, introduction of the multigrid technique into four‐dimensional variational data assimilation is not easy due to its strong dependence on the adjoint model, which has high computational costs in data coding, maintenance, and updating, especially for large‐scale, complex problems. In this study, the multigrid technique was introduced into the nonlinear least squares four‐dimensional variational assimilation (NLS‐4DVar) method, which is an advanced four‐dimensional ensemble‐variational method that can be applied without invoking the adjoint models. The multigrid NLS‐4DVar (MG‐NLS‐4DVar) scheme uses the number of grid points to control the scale, with doubling of this number when moving from coarser to finer grid levels. Furthermore, the MG‐NLS‐4DVar scheme not only retains the advantages of NLS‐4DVar but also sufficiently corrects multiscale errors to achieve a highly accurate analysis. The effectiveness and efficiency of the proposed MG‐NLS‐4DVar scheme were evaluated by one group of single‐observation experiments and one group of comprehensive evaluation experiments using the Advanced Research Weather Research and Forecasting Model. MG‐NLS‐4DVar outperformed NLS‐4DVar, with a lower computational cost.
- Publication
Journal of Geophysical Research. Atmospheres, 2018, Vol 123, Issue 10, p5116
- ISSN
2169-897X
- Publication type
Article
- DOI
10.1029/2017JD027529